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apache/tvm/b547106fdeb634d2fc692d8a516899c4abe6edbc
commit
b547106fdeb634d2fc692d8a516899c4abe6edbc
[log]
author
Lite Ye <liteye859@gmail.com>
Thu Aug 25 11:45:43 2022 -0400
committer
GitHub <noreply@github.com>
Thu Aug 25 08:45:43 2022 -0700
tree
132d713adcc859b0c84ab82984c6186a8a6b28a9
parent
61c034ae27712d5cab4720b3f259df68cf004ac2 [diff]
[CI] github_cc_reviewers: Catch all exceptions so all reviewers can be processed (#12578)

In a recent change, `github.post` throws `RuntimeError` instead of `HTTPError` when the requested reviewer isn't a project collaborator. This prevents other reviewers to be added to the PR, for example, https://github.com/apache/tvm/runs/8001367110?check_suite_focus=true.

This PR changes the caller to catch any exception so the execution won't be interrupted.

Co-authored-by: driazati <9407960+driazati@users.noreply.github.com>
1 file changed
tree: 132d713adcc859b0c84ab82984c6186a8a6b28a9
  1. .github/
  2. 3rdparty/
  3. apps/
  4. ci/
  5. cmake/
  6. conda/
  7. configs/
  8. docker/
  9. docs/
  10. gallery/
  11. golang/
  12. include/
  13. jvm/
  14. licenses/
  15. python/
  16. rust/
  17. src/
  18. tests/
  19. vta/
  20. web/
  21. .asf.yaml
  22. .clang-format
  23. .gitattributes
  24. .gitignore
  25. .gitmodules
  26. .pre-commit-config.yaml
  27. CMakeLists.txt
  28. conftest.py
  29. CONTRIBUTORS.md
  30. Jenkinsfile
  31. KEYS
  32. LICENSE
  33. Makefile
  34. mypy.ini
  35. NEWS.md
  36. NOTICE
  37. pyproject.toml
  38. README.md
  39. version.py
README.md

Open Deep Learning Compiler Stack

Documentation | Contributors | Community | Release Notes

Build Status WinMacBuild

Apache TVM is a compiler stack for deep learning systems. It is designed to close the gap between the productivity-focused deep learning frameworks, and the performance- and efficiency-focused hardware backends. TVM works with deep learning frameworks to provide end to end compilation to different backends.

License

TVM is licensed under the Apache-2.0 license.

Getting Started

Check out the TVM Documentation site for installation instructions, tutorials, examples, and more. The Getting Started with TVM tutorial is a great place to start.

Contribute to TVM

TVM adopts apache committer model, we aim to create an open source project that is maintained and owned by the community. Check out the Contributor Guide.

Acknowledgement

We learned a lot from the following projects when building TVM.

  • Halide: Part of TVM's TIR and arithmetic simplification module originates from Halide. We also learned and adapted some part of lowering pipeline from Halide.
  • Loopy: use of integer set analysis and its loop transformation primitives.
  • Theano: the design inspiration of symbolic scan operator for recurrence.